The evolution of machine learning (ML) algorithms provides researchers and engineers with state-of-the-art tools to dynamically model complex relationships. The design and operation of municipal solid waste (MSW) management systems require accurate estimation of generation rates. In this study, we applied rapid, non-linear and non-parametric data driven ML algorithms independently, multi-layer perceptron artificial neural network (MLP-ANN) and support vector regression (SVR) models to predict annual MSW generation rates in Bahrain. Models were trained and tested with MSW generation data for period of 1997–2019. The population, gross domestic product, annual tourist numbers, annual electricity consumption and total annual CO₂ emissions were selected as explanatory variables and incorporated into developed models. The zero score normalization (ZSN) and minimum maximum normalization (MMN) methods were utilized to improve the quality of data and subsequently enhances the performance of ML algorithms. Statistical metrics were employed to discriminate performance of MLP-ANN and SVR models. The linear, polynomial, radial basis function (RBF) and sigmoid kernel functions were investigated to find the optimal SVR model. Results showed that RBF-SVR model with R² value of 0.97% and 4.82% and absolute forecasting error (AFE) for the period of 2008 and 2019 exhibits superior prediction and robustness in comparison to MLP-ANN. The efficacy of MLP-ANN model was also reasonably successful with R² value of 0.94. It was shown that MMN pre-processing generated optimal MLP-ANN model while ZSN pre-processing produced optimal RBF-SVR model. This work also highlights the importance of application of ML modelling approaches to plan and implement their roadmap for waste management systems by policymakers.

机器学习 (ML) 算法的发展为研究人员和工程师提供了最先进的工具来动态建模复杂的关系。城市固体废物 (MSW) 管理系统的设计和运行需要准确估算产生率。在这项研究中，我们独立应用快速、非线性和非参数数据驱动的 ML 算法、多层感知器人工神经网络 (MLP-ANN) 和支持向量回归 (SVR) 模型来预测巴林的年 MSW 生成率。使用 1997-2019 年期间的 MSW 生成数据对模型进行了训练和测试。人口、国内生产总值、年旅游人数、年用电量和年CO _2总量排放物被选为解释变量，并被纳入已开发的模型中。零分数归一化 (ZSN) 和最小最大归一化 (MMN) 方法用于提高数据质量并随后增强 ML 算法的性能。使用统计指标来区分 MLP-ANN 和 SVR 模型的性能。研究了线性、多项式、径向基函数 (RBF) 和 sigmoid 核函数，以找到最佳 SVR 模型。结果表明，与MLP-ANN 相比，具有 0.97% 和 4.82% 的R ^{2值和绝对预测误差 (AFE) 的 RBF-SVR 模型在 2008 年和 2019 年期间表现出更好的预测和鲁棒性。}MLP-ANN 模型的功效在R ²下也相当成功值为 0.94。结果表明，MMN 预处理生成了最优的 MLP-ANN 模型，而 ZSN 预处理生成了最优的 RBF-SVR 模型。这项工作还强调了应用 ML 建模方法来规划和实施政策制定者的废物管理系统路线图的重要性。